EAR: Edge-Aware Reconstruction of 3-D vertebrae structures from bi-planar X-ray images

TL;DR

This paper introduces EAR, an edge-aware neural network that improves 3-D vertebrae reconstruction from 2-D X-ray images by emphasizing edge and shape preservation, outperforming existing models on multiple metrics.

Contribution

The study presents a novel edge-aware reconstruction network with specialized modules and loss functions, enhancing 3-D spine reconstruction accuracy from bi-planar X-ray images.

Findings

Outperforms four state-of-the-art models on three datasets.

Achieves significant improvements in MSE, MAE, Dice, SSIM, PSNR, and frequency distance metrics.

Provides precise 3-D spatial information for surgical planning.

Abstract

X-ray images ease the diagnosis and treatment process due to their rapid imaging speed and high resolution. However, due to the projection process of X-ray imaging, much spatial information has been lost. To accurately provide efficient spinal morphological and structural information, reconstructing the 3-D structures of the spine from the 2-D X-ray images is essential. It is challenging for current reconstruction methods to preserve the edge information and local shapes of the asymmetrical vertebrae structures. In this study, we propose a new Edge-Aware Reconstruction network (EAR) to focus on the performance improvement of the edge information and vertebrae shapes. In our network, by using the auto-encoder architecture as the backbone, the edge attention module and frequency enhancement module are proposed to strengthen the perception of the edge reconstruction. Meanwhile, we also combine four loss terms, including reconstruction loss, edge loss, frequency loss and projection loss. The proposed method is evaluated using three publicly accessible datasets and compared with four state-of-the-art models. The proposed method is superior to other methods and achieves 25.32%, 15.32%, 86.44%, 80.13%, 23.7612 and 0.3014 with regard to MSE, MAE, Dice, SSIM, PSNR and frequency distance. Due to the end-to-end and accurate reconstruction process, EAR can provide sufficient 3-D spatial information and precise preoperative surgical planning guidance.